Ultrasound imaging systems generate ultrasonic echoes from deliberately launched diagnostic sound waves into tissue. The ultrasonic echoes are attenuated in proportion to the distance that the sound waves must travel to reach the reflector, plus the distance that the resulting echoes must travel back to reach the receiver. The ultrasounds are displayed on a screen, providing medical information for the operator.
Assessing the image quality of Ultrasound images has been a subjective science at best. What seems like a "good" quality image to one doctor may be described as a "poor" quality image to another. The subjective nature of image quality has also made it difficult to accurately judge the variation of image quality of a system over time. To overcome the subjective nature of image quality assessment, the principle of Quantitative Image Quality (QIQ) has been developed to correspond measurable numbers to the quality of an image. Generally, there are three characteristics of image quality that QIQ measures-Acoustic Peak To Noise (Signal-to-Noise), Detail Resolution (20 dBWidth) and Cystic Clearing (Contrast Resolution). To date, the means of measuring these quantities has been time-consuming and cumbersome. First, an engineer acquires many sets data from an Ultrasound image, via a "screen-grabber" or other off-line tool. The settings of the Ultrasound system to be set precisely in order to ensure that accurate data was collected. Once the raw data is collected, the data is analyzed and interpreted (again by off-line tools), finally producing the QIQ numbers from the manipulated data. This entire process, requiring the expertise of a very knowledgeable Ultrasound engineer, is a lengthy process.
It is seen then that it would be desirable to be able to automatically, using only the Ultrasound machine and a phantom, determine QIQ statistics in a quick and accurate manner.